Wind Driven Optimization Research Articles

A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer

The multilevel thresholding is one important operation in computer vision, which is a subfield of artificial intelligence (AI), used to understand and interpret the data in the real world. The existing entropic methods, based on the histogram of an image for the multilevel thresholding mostly deal with the maximization of the entropic information excluding the shred boundary, which reduces the accuracy. These problems lead to the poor thresholding accuracy and a lower speed. To address the problem, we propose a novel interdependence based technique that uses the shred boundary, which is a minimization problem. A firsthand objective function is investigated, which takes care of the shred boundary. The traditional multilevel thresholding techniques are computationally expensive due to the exhaustive search process, an alternate method is to use the evolutionary computation based on a nature-inspired algorithm. In this paper, a new optimizer called adaptive equilibrium optimizer (AEO) is also proposed for multilevel thresholding, an improvement over the basic equilibrium optimizer (EO) by implementing an adaptive decision making of dispersal for nonperformer search agents. The AEO performance is compared with state-of-the-art algorithms — equilibrium optimizer (EO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), squirrel search algorithm (SSA) and the wind driven optimization (WDO) algorithm, using standard benchmark functions. Based on the qualitative and quantitative analysis, the AEO outperformed EO, GWO, WOA, SSA, and WDO. The optimal thresholds are obtained by minimizing the objective function using the AEO. For the experiment, 500 images of the BSDS 500 dataset are considered. Popular metrics such as the peak signal to noise ratio (PSNR), structural similarity index (SSIM), and the feature similarity index (FSIM) are considered for a quantitative analysis. Remarkable differences in the thresholding accuracy are observed with a simultaneously decreasing computational complexity. The merits of the paper are highlighted to ensure its future use in the world of engineering applications using soft computing, a subfield of the AI.

Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid.

There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.

Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid

Accurate electric load forecasting is important due to its application in the decision making and operation of the power grid. However, the electric load profile is a complex signal due to the non-linear and stochastic behavior of consumers. Despite much research conducted in this area; still, accurate forecasting models are needed. In this article, a novel hybrid short-term electric load forecasting model is proposed. The proposed model is an integrated framework of data pre-processing and feature selection module, training and forecasting module, and an optimization module. The data pre-processing and feature selection module is based on modified mutual information (MMI) technique, which is an improved version of the mutual information technique, used to select abstractive features from historical data. The training and forecasting module is based on factored conditional restricted Boltzmann machine (FCRBM), which is a deep learning model, empowered via learning to forecast the future electric load. The optimization module is based on our proposed genetic wind-driven (GWDO) optimization algorithm, which is used to fine-tune the adjustable parameters of the model. The accuracy of the proposed framework is evaluated through historical hourly load data of three USA power grids, taken from publicly available PJM electricity market. The proposed model is validated by comparing it with four recent forecasting models like Bi-level, mutual information-based artificial neural network (MI-ANN), ANN-based accurate and fast converging (AFC-ANN), and long short-term memory (LSTM) in terms of accuracy and convergence rate.

An Adaptive Wind-Driven Optimization Algorithm for Extracting the Parameters of a Single-Diode PV Cell Model

This paper presents a new methodology to extract the unknown parameters of a single-diode photovoltaic (PV) cell model. The first contribution of this paper is the development and implementation of a new version of the wind-driven optimization algorithm, called an adaptive wind-driven optimization (AWDO) algorithm. The advantages of the AWDO algorithm are: 1) accurate extraction of the global values of the optimized PV parameters in changing weather conditions, which is achieved by building solutions from random operations; and 2) capability of handling the given complex multi-modal and multi-dimensional optimization problems. The second contribution is the identification of a generalization model to generalize the extracted parameters of a single-diode PV cell model. That provides an ability of the proposed methodology to work with any I–V characteristic curve of PV cells and at any weather condition on a 15-min basis. To validate the proposed methodology, it has been tested for 1307 I–V characteristic curves of a PV module at various weather conditions on a 15-min basis. Additionally, its accuracy and computational efficiency are verified and compared with five well-known existing extraction methods: Villalva's model, particle swarm optimization, biogeography-based optimization, Gang's model, and bacterial foraging optimization by both simulation and outdoor measurements. The results show that the AWDO algorithm can provide the extracted five parameters with an acceptable range of accuracy and faster than the aforementioned models. Therefore, the proposed methodology (AWDO based on Chenlo's model) can be confidently recommended as a reliable, feasible, valuable, and fast optimization algorithm for parameter extraction of a single-diode PV cell model.

Optimal modification of heating, ventilation, and air conditioning system performances in residential buildings using the integration of metaheuristic optimization and neural computing

This study pursues optima modification of heating, ventilating, and air conditioning (HVAC) systems embedded in residential buildings through predicting heating load (HL) and cooling load (CL). This purpose is carried out by employing four wise metaheuristic algorithms, namely wind-driven optimization (WDO), whale optimization algorithm (WOA), spotted hyena optimization (SHO), and salp swarm algorithm (SSA) synthesized with a multi-layer perceptron (MLP) neural work in order to overcome the computational shortcomings of this model. The used dataset consists of overall height, glazing area, orientation, relative compactness, wall area, glazing area distribution, roof area, and surface area as independent factors, and the HL and CL as target factors. The results indicated a high capability of all four metaheuristic ensembles for understanding the non-linear relationship between the mentioned factors. Meanwhile, a comparison between the used models revealed that SSA-MLP (ErrorHL = 1.9178 and ErrorCL = 2.1830) is the most accurate model, followed by WDO-MLP (ErrorHL = 1.9863 and ErrorCL = 2.2424), WOA-MLP (ErrorHL = 2.1921 and ErrorCL = 2.5390), and SHO-MLP (ErrorHL = 3.1092 and ErrorCL = 4.5930). Regarding the satisfying accuracy of the SSA-based ensemble, it can be a reliable tool for estimating the HL and CL for future smart city planning.

Hybridizing four wise neural-metaheuristic paradigms in predicting soil shear strength

Due to the vital significance of precise determination of soil shear strength (SSS) in many civil engineering projects, this study is dedicated to proposing novel intelligent models for estimating this parameter. To this end, elephant herding optimization (EHO), shuffled frog leaping algorithm (SFLA), salp swarm algorithm (SSA), and wind-driven optimization (WDO) are synthesized with artificial neural network (ANN) to create neural ensembles. The results indicated the efficiency of metaheuristic science for dealing with the non-linear analysis of the SSS and influential soil parameters. Also, a comparison between the models revealed that the SSA-MLP (Error = 0.0386 and Correlation = 0.8219) presents the most efficient prediction, followed by WDO-MLP (Error = 0.0403 and Correlation = 0.8025), SFLA-MLP (Error = 0.0408 and Correlation = 0.7559), and EHO-MLP (Error = 0.0436 and Correlation = 0.7195). Therefore, the proposed SSA-MLP can function as a reliable substitute for traditional approaches in prediction of the SSS.

Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

The prediction aptitude of an artificial neural network (ANN) is improved by incorporating two novel metaheuristic techniques, namely, the shuffled frog leaping algorithm (SFLA) and wind-driven optimization (WDO), for the purpose of soil shear strength (simply called shear strength) simulation. Soil information of the Trung Luong national expressway project (Vietnam) including depth of the sample (m), percentage of sand, percentage of silt, percentage of clay, percentage of moisture content, wet density (kg/m3), liquid limit (%), plastic limit (%), plastic index (%), liquidity index, and the shear strength (kPa) was collocated through a field survey. After constructing the hybrid ensembles of SFLA–ANN and WDO–ANN, both models were optimized in terms of complexity using a population-based trial-and error-scheme. The learning quality of the ANN was compared with both improved versions to examine the effect of the used metaheuristic techniques. In this phase, the training error dropped by 14.25% and 28.25% by applying the SFLA and WDO, respectively. This reflects a significant improvement in pattern recognition ability of the ANN. The results of the testing data revealed 25.57% and 39.25% decreases in generalization (i.e., testing) error. Moreover, the correlation between the measured and predicted shear strengths (i.e., the coefficient of determination) rose from 0.82 to 0.89 and 0.92, which indicates the efficiency of both SFLA and WDO metaheuristic techniques in optimizing the ANN.

Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.

An efficient optimal multilevel image thresholding with electromagnetism-like mechanism

Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.

Wind Driven Based Heuristic Solution for Multiobjective Traffic Grooming in Optical Networks

One of the major problem faced by the optical networks is traffic grooming. The large size of data and the speedy transmission leads to traffic grooming. In this research paper, the wind driven optimization (WDO) technique has been used for optimizing the traffic grooming in the optical network. The paper describes the model of optical systems, WDM concepts, optimization and its components. Then, the issue of traffic grooming is discoursed in paper that includes information on its types, parameters, objective function, variables, and related constraints. The results achieved with the implementation of WDO algorithm proved to be cost effective. Two multiobjective indicators are used in the study. Results have been compared with multiobjective evolution algorithm based on decomposition (MOEA/D) and DEPT, i.e., differential evolution with Pareto tournaments in terms of multi-objective indicator hypervolume. Hypervolume is the effective quantative parameter that calculate the proximity of the calculated solution to the true pareto front. Multiobjective indicator set coverage values have also been evaluated using WDO and are compared with the setcoverage values obtained with the use of shortest path routing and first fit wavelength algorithm. Results obtained were quite promising in terms of hypervolume and setcoverage values both. Thus, WDO algorithm can be stated as optimizer as compared to existing techniques DEPT, MOEA/D and shortest path routing and first fit wavelength assignment method.

Spatiotemporal variation analysis of regional flood disaster resilience capability using an improved projection pursuit model based on the wind-driven optimization algorithm

Abstract Due to the weak methods available for evaluation of the resilience of regional flood disaster systems and the lack of research on the driving mechanism of resilience, by exploring the principles of regional flood disaster resilience and constructing a suitable evaluation index system, the wind driven optimization (WDO) algorithm was introduced, and an improved projection pursuit (PP) evaluation model of flood disaster resilience was proposed. Twelve farms under the Heilongjiang Agricultural Reclamation Hongxinglong Administration Bureau were included in the research area. A total of 43 primary indicators were selected from four criteria to describe the natural environment, culture, society, economic development and flood control technologies. The R clustering factor analysis method was used to determine 15 optimal indexes. The improved PP model based on the WDO algorithm (WDO-PP) was used to evaluate the flood disaster resilience of 12 farms. The results showed that the number of farms with a level IV rating on flood resilience decreased from 25% to 8.3% from 2002 to 2009. In 2009–2016, with the exception of the Bawuer and Shuguang farms, the flood disaster resilience index decreased, and that of the remaining farms increased. In 2002–2016, the Wujiuqi, Shuangyashan, Shuguang and Hongqiling farms in the central region of the Hongxinglong Administration Bureau were less resilient to disasters, and the farms that responded better to flood disasters were mainly located in the eastern or western Hongxinglong Administration Bureau near a river. Further analysis shows that the forest coverage rate, paddy field coverage ratio, shelter forest area ratio, proportion of primary industry, agricultural water use efficiency, and irrigation and drainage capacity were the key drivers of the flood disaster resilience in the Hongxinglong Management Bureau. Based on the Rastrigin and Schaffer functions, the results show that the success rate of the WDO algorithm is 100% over 10 iterations of the optimization calculation of the test function, while the success rate of the other two algorithms is relatively inadequate; however, in terms of value and standard deviation, both are better than adaptive particle swarm optimization (APSO) and adaptive genetic algorithm (AGA) algorithms. Moreover, in the convergence curve, the WDO algorithm converges fast, the number of iterations can achieve the optimal effect on average 3–5 times, and the AGA and APSO algorithms need more than 40 iterations to achieve the best-seeking effect. Taking the index of agricultural water use efficiency in Bawuer farm as an example, the index weight is greater than 60% and the utilization rate of agricultural water is more than 98%, which is closer to reality. Therefore, the evaluation results of the flood disaster resilience evaluation model proposed in this study are more accurate: WDO-PP>(adaptive genetic algorithm) AGA-PP>(adaptive particle swarm optimization algorithm)APSO-PP. In conclusion, the WDO-PP model has certain reference value for flood disaster recovery, monitoring and early warning.

Social mimic optimization algorithm and engineering applications

Increase in complexity of real world problems has provided an area to explore efficient methods to solve computer science problems. Meta-heuristic methods based on evolutionary computations and swarm intelligence are instances of techniques inspired by nature. This paper presents a novel social mimic optimization (SMO) algorithm inspired by mimicking behavior to solve optimization problems. The proposed algorithm is evaluated using 23 test functions. Obtained results are compared with 14 known optimization algorithms including Whale optimization algorithm (WOA), Grasshopper optimization algorithm (GOA), Particle Swarm Optimization (PSO), Stochastic fractal search (SFS), Grey Wolf Optimizer (GWO), Optics Inspired Optimization (OIO), League Championship Algorithm (LCA), Wind Driven Optimization (WDO), Harmony search (HS), Firefly Algorithm (FA), Artificial Bee Colony (ABC), Biogeography Based Optimization (BBO), Bat Algorithm (BA), and Teaching Learning Based Optimization (TLBO). Obtained results indicate higher capability of the SMO algorithm in solving high-dimensional decision variables. Furthermore, SMO is used to solve two classic engineering design problems. Three important features of SMO are simple implementation, solving optimization problems with minimum population size and not requiring control parameters. Results of various evaluations show superiority of the proposed method in finding the optimal solution with minimum function evaluations. This superiority is achieved based on reducing number of initial population. The proposed method can be applied to applications like automatic evolution of robotics, automatic control of machines and innovation of machines in finding better solutions with less cost.

BWDO algorithm and its application in antenna array and pixelated metasurface synthesis

This article introduces a novel, binary version of wind driven optimisation (WDO) called binary WDO (BWDO), which can be specifically used for solving binary valued problems such as antenna array and metasurface synthesis. Validation of proposed algorithm is done with standard benchmark functions. To demonstrate the efficiency of BWDO, examples of thinned antenna array and metasurface synthesis are considered. Thinned antenna array problem is solved by formulating single and multi-objective optimisation problems which achieve minimum SLL of −22.30 dB and −20.28 dB respectively, as compared to −13.2 dB by periodic array. Further, the proposed BWDO algorithm is interface with Ansys-HFSS and three design examples of metasurfaces such as MPA and AMC are further considered to exemplify the proficiency of BWDO. Simulation of optimised MPA exhibits five discrete bands of high absorptions at 8.7, 9.7, 14.6, 15.3 and 16.5 GHz with simulation reflection co-efficient value of −11.46, −10.00, −12.17, −16.19 and −20.58 dB which is less than −10 dB reflection under normal incidence of wave. Synthesised single and dual band AMC has in-phase reflection bandwidth of 0.58 and 0.34, 0.16 GHz at central frequency of 5.21 and 5.07, 6.49 GHz respectively. The measured results almost matched with the respective simulated results with slight variation due to fabrication tolerance.

Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers

This article describes an optimization methodology based on a model of competitiveness between different metaheuristic methods. The main contribution is a strategy to dynamically find the algorithm that obtains the best result based on the competitiveness of methods to solve a specific problem using different performance metrics depending on the problem. The algorithms used in the preliminary tests are: the firefly algorithm (FA), which is inspired by blinking fireflies; wind-driven optimization (WDO), which is inspired by the movement of the wind in the atmosphere, and in which the positions and velocities of the wind packages are updated; and finally, drone squadron optimization (DSO)—the inspiration for this method is new and interesting—based on artifacts, where drones have a command center that sends information to individual drones and updates their software to optimize the objective function. The proposed model helps discover the best method to solve a specific problem, and also reduces the time that it takes to search for methods before finding the one that obtains the most satisfactory results. The main idea is that with this competitiveness approach, methods are tested at the same time until the best one to solve the problem in question is found. As preliminary tests of the model, the optimization of the benchmark mathematical functions and membership functions of a fuzzy controller of an autonomous mobile robot was used.

New framework for hyperspectral band selection using modified wind-driven optimization algorithm

ABSTRACT The presence of irrelevant and highly correlated spectral bands significantly reduces the classification accuracy of the hyperspectral images. Therefore, the selection of suitable bands from the set of available spectral bands plays a crucial role in improving the classification accuracy. In this paper, a novel band selection approach is proposed based on nature inspired meta-heuristic algorithm to mitigate the effect of curse of dimensionality. Wind-driven optimization (WDO), among other meta-heuristic algorithms, has proven to be more efficient in solving global optimization problems. However, WDO is prone to premature convergence when solving the global optimization problem due to loss of diversity of air particles. Therefore, a modified WDO (MWDO) is proposed for band selection, which is able to avoid the premature convergence and control the exploration–exploitation search trade-off. Finally, in order to further improve the performance of the classification, the selected bands are fed into the deep learning architecture to extract the high-level useful features. The experiments are carried on three widely used standard datasets such as Indian Pines, Pavia University, and Salinas. The experimental results show that the proposed approach selects an optimal subset of bands with good convergence characteristics and provide high classification accuracy with fewer bands in comparison with other approaches. The proposed method achieves an overall accuracy of 93.26%, 94.76%, and 95.96% for Indian Pines, Pavia University, and Salinas datasets, respectively.

Utilisation of plug‐in electric vehicles for frequency regulation of multi‐area thermal interconnected power system

The application of plug-in electric vehicles (PEVs) is assumed to be wide in power system in regulating the system frequency in the near future. This paper provides an aggregate model of an interconnected multi-area thermal system with the incorporation of PEVs for frequency control in each of the three areas. Two degree of freedom proportional–integral–derivative (2DOF-PID) controller has been utilised for robust secondary control in all the three control areas. An optimisation technique inspired by nature named as wind-driven optimisation (WDO) technique is employed to decide the optimal values of the controller gains. The effectiveness of WDO optimised 2DOF-PID controller is verified by performing various comparative analysis with conventional PID controller under nominal system condition and random loading condition. An analysis has also been carried out to evaluate the system performance with variation in state of the integrated PEVs. The impact of introducing PEVs in the system in frequency regulation has been vigorously studied under different system conditions such as nominal, random loading condition, and simultaneous perturbation in two and three areas to testify their advantages. The comparisons reveal that with the integration of PEVs in the system, the system dynamics gets enhanced to a large extent.

A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation.

Multilevel thresholding segmentation of color images is an important technology in various applications which has received more attention in recent years. The process of determining the optimal threshold values in the case of traditional methods is time-consuming. In order to mitigate the above problem, meta-heuristic algorithms have been employed in this field for searching the optima during the past few years. In this paper, an effective technique of Electromagnetic Field Optimization (EFO) algorithm based on a fuzzy entropy criterion is proposed, and in addition, a novel chaotic strategy is embedded into EFO to develop a new algorithm named CEFO. To evaluate the robustness of the proposed algorithm, other competitive algorithms such as Artificial Bee Colony (ABC), Bat Algorithm (BA), Wind Driven Optimization (WDO), and Bird Swarm Algorithm (BSA) are compared using fuzzy entropy as the fitness function. Furthermore, the proposed segmentation method is also compared with the most widely used approaches of Otsu’s variance and Kapur’s entropy to verify its segmentation accuracy and efficiency. Experiments are conducted on ten Berkeley benchmark images and the simulation results are presented in terms of peak signal to noise ratio (PSNR), mean structural similarity (MSSIM), feature similarity (FSIM), and computational time (CPU Time) at different threshold levels of 4, 6, 8, and 10 for each test image. A series of experiments can significantly demonstrate the superior performance of the proposed technique, which can deal with multilevel thresholding color image segmentation excellently.

Optimal line flows based on voltage profile, power loss, cost and conductor temperature using coordinated controlled UPFC

In the context of flexible operation and bidirectional line flows in modern transmission systems, suitable line flows are essential for safe, secure and stable power systems. Improper line flows may deteriorate stability and longevity of transmission lines. To avoid line outage and faults, improvement in thermal margin of conductors by regulating line flows is a practical consideration. To maintain and control the line flows, the unified power flow controller (UPFC) is the most appropriate compensating device. Thus, in this study, line flows are considered as state variables to optimise several functions, like, voltage deviation index, active and reactive power losses, temperature of conductors and cost of UPFC. The UPFC is modelled with practical losses like switching, coupling and internal transmission losses. Suitable line flows are obtained for the 30-bus and 57-bus test systems by applying an adaptive-version of the wind-driven optimisation (AWDO) algorithm. The AWDO is designed using two innovative tuning parameters: a decaying friction coefficient to reduce the probability of stagnation, and an adaptive velocity function to maintain diversity. The results are compared among different cases of line flows, validating the necessity of the technical challenge. The performance of AWDO is compared with other techniques, suggesting its superiority over other methods.

Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance

This paper proposes an accurate global maximum power point tracking (MPPT) technique based on Wind Driven Optimization (WDO) algorithm for photovoltaics system (PVS) under non-uniform solar irradiance. Under non-uniform solar irradiance or partial shading conditions (PSC), the power-voltage curve of the PVS has several maximum power points (MPPs), one of which is assumed to be the global peak. This condition complicates the optimal MPP tracking and decreases of the PVS efficiency. Conventional MPPT methods, such as Perturb & Observe, Hill Climbing and Incremental Inductance are likely to be trapped at one of the local MPPs. The major disadvantage of these methods is their incompetence to specify the global MPP under the above mentioned situation. To overcome this problem, global MPPT techniques based on modern optimization algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Harmony Search Algorithm (HSA), Bat Algorithm (Bat), Sine-Cosine Algorithm (SCA), Cuckoo Search (CS), and Genetic Algorithm (GA) are previously developed. The main contribution of this research is to introduce a novel global MPPT based on WDO algorithm. A comprehensive statistical evaluation of WDO, PSO, DE, HSA, Bat, SCA, CS and GA is carried out under different scenarios of shadowing conditions. Seven statistical metrics including the relative error, root mean square error, mean absolute error, standard deviation, success rate, convergence time and efficiency are used to evaluate the powerfulness and the feasibility of the proposed compared to the others. Therefore, the proposed MPPT based on WDO algorithm is considered to be the most effective and superior optimization tool compared to the corresponding ones.

Optimal placement of grid-connected photovoltaic generators in a power system for voltage stability enhancement

The high penetration of photovoltaic (PV) generation can cause many technical issues such as power quality and impact on the power system voltage stability. To improve voltage stability and reducing power loss in a power system with PV generators, appropriate planning of PV generators is considered by optimal placement of PV. Thus, an effective heuristic optimization technique such as the Wind Driven Optimization (WDO) technique is applied for determining optimal location of PV generators in a power system. For determining the optimal location of PV, the objective function considers maximizing the Improved Voltage Stability Index. The proposed method for optimal location of PV generators is implemented on the IEEE 118 and 30 bus transmission systems and the 69-radial distribution system. The optimization results show that integrating PV into the test systems improves voltage stability in the system. Comparing the performance of the WDO with the particle swarm optimization technique, it is shown that the WDO technique gives faster convergence.